Neural approaches to Natural Language Generation (NLG) have been promising for goal-oriented dialogue. One of the challenges of productionizing these approaches, however, is the ability to control response quality, and ensure that generated responses are acceptable. We propose the use of a generate, filter, and rank framework, in which candidate responses are first filtered to eliminate unacceptable responses, and then ranked to select the best response. While acceptability includes grammatical correctness and semantic correctness, we focus only on grammaticality classification in this paper, and show that existing datasets for grammatical error correction don't correctly capture the distribution of errors that data-driven generators are likely to make. We release a grammatical classification and semantic correctness classification dataset for the weather domain that consists of responses generated by 3 data-driven NLG systems. We then explore two supervised learning approaches (CNNs and GBDTs) for classifying grammaticality. Our experiments show that grammaticality classification is very sensitive to the distribution of errors in the data, and that these distributions vary significantly with both the source of the response as well as the domain. We show that it's possible to achieve high precision with reasonable recall on our dataset.

Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) which are commonly accepted as magnetars, i.e.\ magnetically powered neutron stars with decaying super-strong fields. Yet some other young INS are identified as central compact objects (CCOs) and X-ray dim isolated neutron stars (XDINs) which are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analyzing the distribution of these pulsar families in period $P$ and period derivative $\dot{P}$ parameter space. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature and proper motion of all discovered components. We verify that DPGMM is robust and provides hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.

Finding the source of a disturbance or fault in complex systems such as industrial chemical processing plants can be a difficult task and consume a significant number of engineering hours. In many cases, a systematic elimination procedure is considered to be the only feasible approach but can cause undesired process upsets. Practitioners desire robust alternative approaches. This paper presents an unsupervised, data-driven method for ranking process elements according to the magnitude and novelty of their influence. Partial bivariate transfer entropy estimation is used to infer a weighted directed graph of process elements. Eigenvector centrality is applied to rank network nodes according to their overall effect. As the ranking of process elements rely on emerging properties that depend on the aggregate of many connections, the results are robust to errors in the estimation of individual edge properties and the inclusion of indirect connections that do not represent the true causal structure of the process. A monitoring chart of continuously calculated process element importance scores over multiple overlapping time regions can assist with incipient fault detection. Ranking results combined with visual inspection of information transfer networks is also useful for root cause analysis of known faults and disturbances. A software implementation of the proposed method is available.

WASHINGTON - Embattled U.S. aviation giant Boeing on Thursday insisted on the "fundamental safety" of its 737 Max aircraft but pledged to take all necessary steps to ensure the jets' airworthiness. The statements came hours after Ethiopian officials said pilots of a doomed plane that crashed last month, leaving 157 people dead, had followed the company's recommendations. The preliminary findings released Thursday by transportation authorities in Addis Ababa put the American aircraft giant under even greater pressure to restore public trust amid mounting signs the company's onboard anti-stall systems were at fault in crashes involving its formerly top-selling 737 Max aircraft -- incidents that left nearly 350 people dead in less than five months. "We remain confident in the fundamental safety of the 737 Max," CEO Dennis Muilenburg said in a statement, adding that impending software fixes would make the aircraft "among the safest airplanes ever to fly." Muilenburg also acknowledged, however, that an "erroneous activation" of Boeing's Maneuvering Characteristics Augmentation System had occurred. The system is designed to prevent stalls but may have forced the Ethiopian and Indonesian jets into the ground.

Is all of machine learning supervised to some degree? The field of machine learning has traditionally been categorized pedagogically into $supervised~vs~unsupervised~learning$; where supervised learning has typically referred to learning from labeled data, while unsupervised learning has typically referred to learning from unlabeled data. In this paper, we assert that all machine learning is in fact supervised to some degree, and that the scope of supervision is necessarily commensurate to the scope of learning potential. In particular, we argue that clustering algorithms such as k-means, and dimensionality reduction algorithms such as principal component analysis, variational autoencoders, and deep belief networks are each internally supervised by the data itself to learn their respective representations of its features. Furthermore, these algorithms are not capable of external inference until their respective outputs (clusters, principal components, or representation codes) have been identified and externally labeled in effect. As such, they do not suffice as examples of unsupervised learning. We propose that the categorization `supervised vs unsupervised learning' be dispensed with, and instead, learning algorithms be categorized as either $internally~or~externally~supervised$ (or both). We believe this change in perspective will yield new fundamental insights into the structure and character of data and of learning algorithms.

The idea behind the guillotine is this: If you're going to execute someone, you may as well do it efficiently and humanely, at least by 18th-century standards. Decapitating the condemned with an ax or sword may take a few swings--unacceptable for carrying out justice in a "civilized" society. The guillotine, on the other hand, is downright surgical, a perversely methodical way to end a life. Now mosquitoes are getting the same treatment in the pursuit of a vaccine for malaria, a disease that killed 440,000 people in 2016. To produce a vaccine for mass deployment, biotech firm Sanaria has to decapitate and dissect out the salivary glands, which hold the malaria-causing parasite, for each individual mosquito--by hand.

Google's taking a more'natural' approach to it's April Fool's Day this year, adding a plant-to-human interface called Google Tulips and a'Snakes' game for Maps in the latest installment of phony and fun products. According to the company, Google Tulips, available only on April 1, allows users to communicate with one of world's most beloved flowers -- the tulip -- to ascertain just what exactly is on their minds. 'Decoding the language of plants and flowers has been a decades-long challenge. But that changes today,' reads the company's statement on its farcical new service. In a video for the April Fool's gag, plants are shown beckoning for more water, sunlight, and even providing a source of conversation for an elderly woman.

Businesses and nonprofits are finding novel ways to employ artificial intelligence in the developing world, using the tools to improve agriculture yields, infant health care, and entrepreneur earnings, according to speakers at MIT Technology Review's EmTech Digital conference in San Francisco on Tuesday. Solomon Assefa, who oversees IBM's research labs in Kenya and South Africa, said the company has been using AI to more accurately predict crop yields in specific regions, based on shifting weather patterns, soil moisture, and other conditions. This insight into growing conditions has helped local farmers raise financing to expand their operations, or make better decisions about the right seeds, appropriate fertilizer, and ideal times to plant and harvest. Separately, the tech giant's research lab has partnered with a startup, Hello Tractor, that links farmers in need of tractors with owners looking to lease equipment. By forecasting demand for the vehicles, IBM has also helped owners raise money to expand their fleet, boosting their profits, Assefa said.

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. As code-switching data and resources are scarce, we list what is available in various code-switched language pairs with the language processing tasks they can be used for. We review code-switching research in various Speech and NLP applications, including language processing tools and end-to-end systems. We conclude with future directions and open problems in the field.

If you're inclined to puns, you might say medical samples are the lifeblood of hospital systems. But if you actually work with them, you know they're more of a headache. Because the same road traffic that keeps you from getting home keeps the couriers charged with moving these tissue and blood samples, collected by the millions daily and often in urgent need of analysis, from completing their missions. So it makes a lot of sense that when the FAA decided to sanction the first revenue-generating drone delivery scheme in the US, it went with one that promises to speed up that process, run by UPS and autonomous drone technology firm Matternet. It makes sense from the tech perspective, too: The cargo is extremely lightweight and compact, allowing the companies involved to focus on the delivery processes and mechanisms rather than trying to manage unwieldy payloads.